Microcredit access is seen as a solution to improving the condition of people experiencing poverty in the developing world (Dahal as well as Fiala, 2020 Imai and others. 2010; Karlan & Zinman, 2011; van Rooyen et al., 2012). This is because households with low incomes and communities are often unable to access a flexible source of income to deal with shocks that can be negative, such as sickness or the death of family members.

Although microcredit can be instrumental in dealing with shocks that are negative, the provision of microloans using conventional channels, like microfinance institutions, is not immediate. In the majority of cases, it could require physical traveling to the branches of the financial services providers. However, this could cause a high cost for transactions, particularly in countries with poor infrastructure where banks are not as numerous (Beck and colleagues. 2009; Francis et al., 2017; Jack & Suri, 2014), thus preventing people from accessing loans quickly. The high costs of financial transactions, in conjunction with the conditions that apply to loan acquisition that include collateral or even prescriptions on how loans are to be used, often deter consumers from taking advantage of microcredit (Francis and Suri. 2017; Stefanelli et al., 2022). As a result, those living in rural areas and poor neighborhoods are unable to avail of official financial assistance (Demirguc-Kunt and co. 2018).

in the world of developing nations, recent advances in technology for financial services (Fintech) and the growth of smartphones (Aker Mbiti and Aker) have resulted in the creation of new products for financial services, like digital credit. With commercial banks, Fintech firms are offering microcredit in the form of digital credit to customers beyond traditional channels, using digital platforms. Digital credit is different in comparison to traditional channels of credit since they are accessible online and instantly via mobile phones or apps in a matter of seconds or even a day without collateral. Furthermore, the process of processing loans as well as credit scoring is now automated (Chen Mazer and Mazer 2016. Pelletier and Pelletier. 2020). Digital credit, for instance, that mobile network operators offer could rely on transactions of consumers who use telecommunications to create credit scores which are essential in assessing the creditworthiness of prospective borrowers (Dalton et al. 2019; Pelletier et al. 2020).

Digital credit is readily available to consumers, and can assist household members and individuals cope with shocks that are negative. But, there’s growing concern about the high rates of interest associated with digital loans that are short-term footnote 1 along with the risk of these loans leading to over-indebtedness (Wamalwa and others. 2019; Wang et al. 2021) and could have implications for welfare. However, the lack of evidence from empirical studies of the effects that digital lending has on the welfare system, specifically in rural areas that have a higher chance of being financially disadvantaged. Thus, the primary goal of this research is to fill in this research gap by investigating whether the development of local digital lending impacts welfare and whether this impact is beneficial to rural communities.

This study investigates the relation between the development of local digital lending and the level of deprivation in Kenya. This research focuses on Kenya due to its leadership part in mobile financial services across Africa (Suri 2017.). Based on both the 2015 as well as 2019 FinAcess studies, I then determine a local digital lending development indicator, which measures the ease at that digital credit is accessible in a specific county, which is an administrative region located in Kenya. Footnote2 Local digital lending is expected to ease accessibility to credit via digital as well as the control of risk that can lead to poverty. The second study links the development of local digital lending variable to the possibility of those who report the presence of poverty within their homes. Particularly, this study is focused on health and nutrition deprivation in order to show the impact of local development in digital lending in achieving the United Nations Sustainable Development Goals (SDGs) 2 and 3. In particular, SDG 2 seeks to help countries end hunger and attain food security, while SDG 3 targets the promotion of healthy living and overall well-being. Additionally, this study examines the impact of digital lending on rural communities.

The estimates from the multilevel regression indicate an association that is negative between the local development of digital lending and food deprivation on one hand, and health-related deprivation on the contrary. The research suggests that local lending developments may reduce the likelihood of deprivation in health and food. These results also suggest that rural people benefit more benefit from this phenomenon over their urban counterparts. The results are dependable to the incorporation of household, individual and regional factors as well as the use using an instrumental multilevel estimation method to account for the endogeneity.